To deal with the anisotropic spacing in 3D image scans, previous networks can be divided into three major categories: First, directly applying 2D networks slice by slice to get the final results [1]}, which is agnostic to large inter-slice spacing. However, due to the internal structural constraints, the 2D networks can hardly capture 3D information. Second, resampling the original images into the same spacing along three dimensions [2]}, [3]}, [4]} and processing them with standard 3D convolutions, which could merge the information among three dimensions effectively. But it will generate artifacts in interpolated slices and obtain reduced segmentation accuracy. Thirdly, applying 2.5D network [5]}, [6]}, which firstly use 2D convolutions separately in three orthogonal views and fuse these multi-view information together in final stage, so that 3D contextual features can be used. However, previous 2.5D networks mainly treat each directions equally thus ignore the anisotropic spacing and organ scales among three dimensions.
